





import numpy as np

UpperCAmelCase_							=				[
    ["""a""", """b""", """c""", """d""", """e"""],
    ["""f""", """g""", """h""", """i""", """k"""],
    ["""l""", """m""", """n""", """o""", """p"""],
    ["""q""", """r""", """s""", """t""", """u"""],
    ["""v""", """w""", """x""", """y""", """z"""],
]
class     UpperCamelCase__					:






		'''simple docstring'''






		def __init__(							self  )       -> Optional[Any]:






									"""simple docstring"""
									lowercase_	:   Union[str, Any]  				=     np.array(snake_case__  )






		def 						snake_case__				(							self,   snake_case__  )       -> List[Any]:






									"""simple docstring"""
									lowercase_				,		lowercase_	:   List[str]  				=     np.where(letter == self.SQUARE  )
									lowercase_	:   Dict  				=     np.concatenate([indexa + 1, indexa + 1]  )
									return indexes






		def 						snake_case__				(							self,   snake_case__,   snake_case__  )       -> int:






									"""simple docstring"""
									lowercase_	:   Tuple  				=     self.SQUARE[indexa - 1, indexa - 1]
									return letter






		def 						snake_case__				(							self,   snake_case__  )       -> str:






									"""simple docstring"""
									lowercase_	:   List[str]  				=     message.lower()
									lowercase_	:   Dict  				=     message.replace(""" """,   """"""  )
									lowercase_	:   Tuple  				=     message.replace("""j""",   """i"""  )

									lowercase_	:   List[Any]  				=     np.empty((2, len(snake_case__  ))  )
									for letter_index in range(len(snake_case__  )  ):
																lowercase_	:   Dict  				=     self.letter_to_numbers(message[letter_index]  )

																lowercase_	:   int  				=     numbers[0]
																lowercase_	:   Tuple  				=     numbers[1]

									lowercase_	:   str  				=     first_step.reshape(2 * len(snake_case__  )  )
									lowercase_	:   Optional[int]  				=     """"""
									for numbers_index in range(len(snake_case__  )  ):
																lowercase_	:   Optional[Any]  				=     int(second_step[numbers_index * 2]  )
																lowercase_	:   str  				=     int(second_step[(numbers_index * 2) + 1]  )
																lowercase_	:   Optional[Any]  				=     self.numbers_to_letter(snake_case__,   snake_case__  )
																lowercase_	:   Any  				=     encoded_message + letter

									return encoded_message






		def 						snake_case__				(							self,   snake_case__  )       -> List[str]:






									"""simple docstring"""
									lowercase_	:   str  				=     message.lower()
									message.replace(""" """,   """"""  )
									lowercase_	:   str  				=     np.empty(2 * len(snake_case__  )  )
									for letter_index in range(len(snake_case__  )  ):
																lowercase_	:   List[str]  				=     self.letter_to_numbers(message[letter_index]  )
																lowercase_	:   List[str]  				=     numbers[0]
																lowercase_	:   Optional[int]  				=     numbers[1]

									lowercase_	:   int  				=     first_step.reshape((2, len(snake_case__  ))  )
									lowercase_	:   int  				=     """"""
									for numbers_index in range(len(snake_case__  )  ):
																lowercase_	:   int  				=     int(second_step[0, numbers_index]  )
																lowercase_	:   Union[str, Any]  				=     int(second_step[1, numbers_index]  )
																lowercase_	:   str  				=     self.numbers_to_letter(snake_case__,   snake_case__  )
																lowercase_	:   Tuple  				=     decoded_message + letter

									return decoded_message
"""simple docstring"""



import os

import pytest
from attr import dataclass


SCREAMING_SNAKE_CASE__:List[str]									=			"""us-east-1"""  # defaults region






@dataclass
class  snake_case__      :
 _snake_case       :					str
 _snake_case       :					Optional[Any]							  =						"""arn:aws:iam::558105141721:role/sagemaker_execution_role"""
 _snake_case       :					Optional[Any]							  =						{
     """task_name""": """mnli""",
     """per_device_train_batch_size""": 16,
     """per_device_eval_batch_size""": 16,
     """do_train""": True,
     """do_eval""": True,
     """do_predict""": True,
     """output_dir""": """/opt/ml/model""",
     """overwrite_output_dir""": True,
     """max_steps""": 500,
     """save_steps""": 5_500,
 }
 _snake_case       :					List[str]							  =						{**hyperparameters, """max_steps""": 1_000}


 @property
 def 						a__   ( self						):
        if self.framework == "pytorch":
               return [
                   {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                   {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
                   {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
               ]
        else:
               return [
                   {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                   {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
                   {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
               ]


 @property
 def 						a__   ( self						):
        return F"{self.framework}-transfromers-test"


 @property
 def 						a__   ( self						):
        return F"./tests/sagemaker/scripts/{self.framework}"






 @property
 def 						a__   ( self						):
        if self.framework == "pytorch":
               return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
        else:
               return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"



@pytest.fixture(scope="class"				)
def 	_lowerCamelCase(	a				):
       __a       =    SageMakerTestEnvironment(framework=request.cls.framework				)
